Gaussian approximation based mixture reduction for joint channel estimation and detection in MIMO systems

Y Jia; C Andrieu; RJ Piechocki; M Sandell

doi:10.1109/TWC.2007.05911

Gaussian approximation based mixture reduction for joint channel estimation and detection in MIMO systems

Y Jia, C Andrieu, RJ Piechocki, M Sandell

Research output: Contribution to journal › Article (Academic Journal)

1 Citation (Scopus)

272 Downloads (Pure)

Abstract

A novel Gaussian approximation based mixture reduction algorithm is proposed for semi-blind joint channel tracking and symbol detection for spatial multiplexing multiple-input multiple-output (MIMO) systems with frequency-flat time-selective channels. The proposed algorithm is based on a modified sequential Gaussian approximation detector (SGA) which takes into account channel uncertainty, and the first order generalized pseudo-Bayesian (GPB1) channel estimator. Simulation results show that the proposed algorithm performs better than the conventional and computationally expensive decision-directed method with Kalman filter based channel estimation and a posteriori probability (APP) symbol detection.

Original language	English
Pages (from-to)	2384 - 2389
Number of pages	6
Journal	IEEE Transactions on Wireless Communications
Volume	6
Issue number	77
DOIs	https://doi.org/10.1109/TWC.2007.05911
Publication status	Published - Jul 2007

Bibliographical note

Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Rose publication type: Journal article

Sponsorship: This work was supported by Toshiba Research Europe Ltd (Bristol), UK.

Terms of use: Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Wireless Communications. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Keywords

joint estimation and detection
MIMO systems
multiple model estimation
multiuser detection
time-varying channels

Access to Document

10.1109/TWC.2007.05911

Jia Trans WComs July2007Accepted author manuscript, 157 KB

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Cite this

Jia, Y., Andrieu, C., Piechocki, RJ., & Sandell, M. (2007). Gaussian approximation based mixture reduction for joint channel estimation and detection in MIMO systems. IEEE Transactions on Wireless Communications, 6(77), 2384 - 2389. https://doi.org/10.1109/TWC.2007.05911

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abstract = "A novel Gaussian approximation based mixture reduction algorithm is proposed for semi-blind joint channel tracking and symbol detection for spatial multiplexing multiple-input multiple-output (MIMO) systems with frequency-flat time-selective channels. The proposed algorithm is based on a modified sequential Gaussian approximation detector (SGA) which takes into account channel uncertainty, and the first order generalized pseudo-Bayesian (GPB1) channel estimator. Simulation results show that the proposed algorithm performs better than the conventional and computationally expensive decision-directed method with Kalman filter based channel estimation and a posteriori probability (APP) symbol detection.",

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note = "Publisher: Institute of Electrical and Electronics Engineers (IEEE) Rose publication type: Journal article Sponsorship: This work was supported by Toshiba Research Europe Ltd (Bristol), UK. Terms of use: Copyright {\textcopyright} 2007 IEEE. Reprinted from IEEE Transactions on Wireless Communications. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. ",

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